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Hadoop von Tom White (2010, Taschenbuch)-

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Hadoop by Tom White (2010, Paperback)
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Neuwertig: Buch, das wie neu aussieht, aber bereits gelesen wurde. Der Einband weist keine ...
ISBN
9781449389734

Über dieses Produkt

Product Identifiers

Publisher
O'reilly Media, Incorporated
ISBN-10
1449389732
ISBN-13
9781449389734
eBay Product ID (ePID)
102797246

Product Key Features

Number of Pages
628 Pages
Publication Name
Hadoop
Language
English
Publication Year
2010
Subject
Programming / Parallel, Client-Server Computing, General, Data Processing
Type
Textbook
Subject Area
Computers
Author
Tom White
Format
Trade Paperback

Dimensions

Item Height
1.5 in
Item Weight
28.5 Oz
Item Length
9.2 in
Item Width
7 in

Additional Product Features

Edition Number
2
Intended Audience
Scholarly & Professional
Dewey Edition
22
Illustrated
Yes
Dewey Decimal
005.74
Table Of Content
Foreword;Preface; Administrative Notes; What's in This Book?; What's New in the Second Edition?; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgments;Chapter 1: Meet Hadoop; 1.1 Data!; 1.2 Data Storage and Analysis; 1.3 Comparison with Other Systems; 1.4 A Brief History of Hadoop; 1.5 Apache Hadoop and the Hadoop Ecosystem;Chapter 2: MapReduce; 2.1 A Weather Dataset; 2.2 Analyzing the Data with Unix Tools; 2.3 Analyzing the Data with Hadoop; 2.4 Scaling Out; 2.5 Hadoop Streaming; 2.6 Hadoop Pipes;Chapter 3: The Hadoop Distributed Filesystem; 3.1 The Design of HDFS; 3.2 HDFS Concepts; 3.3 The Command-Line Interface; 3.4 Hadoop Filesystems; 3.5 The Java Interface; 3.6 Data Flow; 3.7 Parallel Copying with distcp; 3.8 Hadoop Archives;Chapter 4: Hadoop I/O; 4.1 Data Integrity; 4.2 Compression; 4.3 Serialization; 4.4 File-Based Data Structures;Chapter 5: Developing a MapReduce Application; 5.1 The Configuration API; 5.2 Configuring the Development Environment; 5.3 Writing a Unit Test; 5.4 Running Locally on Test Data; 5.5 Running on a Cluster; 5.6 Tuning a Job; 5.7 MapReduce Workflows;Chapter 6: How MapReduce Works; 6.1 Anatomy of a MapReduce Job Run; 6.2 Failures; 6.3 Job Scheduling; 6.4 Shuffle and Sort; 6.5 Task Execution;Chapter 7: MapReduce Types and Formats; 7.1 MapReduce Types; 7.2 Input Formats; 7.3 Output Formats;Chapter 8: MapReduce Features; 8.1 Counters; 8.2 Sorting; 8.3 Joins; 8.4 Side Data Distribution; 8.5 MapReduce Library Classes;Chapter 9: Setting Up a Hadoop Cluster; 9.1 Cluster Specification; 9.2 Cluster Setup and Installation; 9.3 SSH Configuration; 9.4 Hadoop Configuration; 9.5 Security; 9.6 Benchmarking a Hadoop Cluster; 9.7 Hadoop in the Cloud;Chapter 10: Administering Hadoop; 10.1 HDFS; 10.2 Monitoring; 10.3 Maintenance;Chapter 11: Pig; 11.1 Installing and Running Pig; 11.2 An Example; 11.3 Comparison with Databases; 11.4 Pig Latin; 11.5 User-Defined Functions; 11.6 Data Processing Operators; 11.7 Pig in Practice;Chapter 12: Hive; 12.1 Installing Hive; 12.2 An Example; 12.3 Running Hive; 12.4 Comparison with Traditional Databases; 12.5 HiveQL; 12.6 Tables; 12.7 Querying Data; 12.8 User-Defined Functions;Chapter 13: HBase; 13.1 HBasics; 13.2 Concepts; 13.3 Installation; 13.4 Clients; 13.5 Example; 13.6 HBase Versus RDBMS; 13.7 Praxis;Chapter 14: ZooKeeper; 14.1 Installing and Running ZooKeeper; 14.2 An Example; 14.3 The ZooKeeper Service; 14.4 Building Applications with ZooKeeper; 14.5 ZooKeeper in Production;Chapter 15: Sqoop; 15.1 Getting Sqoop; 15.2 A Sample Import; 15.3 Generated Code; 15.4 Database Imports: A Deeper Look; 15.5 Working with Imported Data; 15.6 Importing Large Objects; 15.7 Performing an Export; 15.8 Exports: A Deeper Look;Chapter 16: Case Studies; 16.1 Hadoop Usage at Last.fm; 16.2 Hadoop and Hive at Facebook; 16.3 Nutch Search Engine; 16.4 Log Processing at Rackspace; 16.5 Cascading; 16.6 TeraByte Sort on Apache Hadoop; 16.7 Using Pig and Wukong to Explore Billion-edge Network Graphs;Installing Apache Hadoop; Prerequisites; Installation; Configuration;Cloudera's Distribution for Hadoop;Preparing the NCDC Weather Data;Colophon;
Synopsis
Discover how Apache Hadoop can unleash the power of your data. This comprehensive resource shows you how to build and maintain reliable, scalable, distributed systems with the Hadoop framework -- an open source implementation of MapReduce, the algorithm on which Google built its empire. Programmers will find details for analyzing datasets of any size, and administrators will learn how to set up and run Hadoop clusters. This revised edition covers recent changes to Hadoop, including new features such as Hive, Sqoop, and Avro. It also provides illuminating case studies that illustrate how Hadoop is used to solve specific problems. Looking to get the most out of your data? This is your book. Use the Hadoop Distributed File System (HDFS) for storing large datasets, then run distributed computations over those datasets with MapReduce Become familiar with Hadoop's data and I/O building blocks for compression, data integrity, serialization, and persistence Discover common pitfalls and advanced features for writing real-world MapReduce programs Design, build, and administer a dedicated Hadoop cluster, or run Hadoop in the cloud Use Pig, a high-level query language for large-scale data processing Analyze datasets with Hive, Hadoop's data warehousing system Take advantage of HBase, Hadoop's database for structured and semi-structured data Learn ZooKeeper, a toolkit of coordination primitives for building distributed systems "Now you have the opportunity to learn about Hadoop from a master -- not only of the technology, but also of common sense and plain talk."--Doug Cutting, Cloudera, Discover how Apache Hadoop can unleash the power of your data. This comprehensive resource shows you how to build and maintain reliable, scalable, distributed systems with the Hadoop framework -- an open source implementation of MapReduce, the algorithm on which Google built its empire. Programmers will find details for analyzing datasets of any ......, Discover how Apache Hadoop can unleash the power of your data. This comprehensive resource shows you how to build and maintain reliable, scalable, distributed systems with the Hadoop framework -- an open source implementation of MapReduce, the algorithm on which Google built its empire. Programmers will find details for analyzing datasets of any size, and administrators will learn how to set up and run Hadoop clusters.This revised edition covers recent changes to Hadoop, including new features such as Hive, Sqoop, and Avro. It also provides illuminating case studies that illustrate how Hadoop is used to solve specific problems. Looking to get the most out of your data? This is your book.Use the Hadoop Distributed File System (HDFS) for storing large datasets, then run distributed computations over those datasets with MapReduceBecome familiar with Hadoop's data and I/O building blocks for compression, data integrity, serialization, and persistenceDiscover common pitfalls and advanced features for writing real-world MapReduce programsDesign, build, and administer a dedicated Hadoop cluster, or run Hadoop in the cloudUse Pig, a high-level query language for large-scale data processingAnalyze datasets with Hive, Hadoop's data warehousing systemTake advantage of HBase, Hadoop's database for structured and semi-structured dataLearn ZooKeeper, a toolkit of coordination primitives for building distributed systems"Now you have the opportunity to learn about Hadoop from a master -- not only of the technology, but also of common sense and plain talk."--Doug Cutting, Cloudera
LC Classification Number
QA76.9.F5

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